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Customizing information capture and access

Authors:

Daniela Rus,

Devika SubramanianAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 15, Issue 1

Pages 67 - 101

https://doi.org/10.1145/239041.239048

Published: 01 January 1997 Publication History

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Abstract

This article presents a customizable architecture for software agents that capture and access information in large, heterogeneous, distributed electronic repositories. The key idea is to exploit underlying structure at various levels of granularity to build high-level indices with task-specific interpretations. Information agents construct such indices and are configured as a network of reusable modules called structure detectors and segmenters. We illustrate our architecture with the design and implementation of smart information filters in two contexts: retrieving stock market data from Internet newsgroups and retrieving technical reports from Internet FTP sites.

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Cited By

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Yang JXiong Z(2020)Analysis and research of news gathering and editing process based on data Mining2020 2nd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML51583.2020.00053(220-223)Online publication date: Oct-2020
https://doi.org/10.1109/ICAML51583.2020.00053
Tsai WChen ABaldoni R(2008)Service oriented architecture for financial customer relationship managementProceedings of the second international conference on Distributed event-based systems10.1145/1385989.1386027(301-304)Online publication date: 1-Jul-2008
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Embley DHurst MLopresti DNagy G(2006)Table-processing paradigms: a research surveyInternational Journal of Document Analysis and Recognition (IJDAR)10.1007/s10032-006-0017-x8:2-3(66-86)Online publication date: 9-May-2006
https://doi.org/10.1007/s10032-006-0017-x
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Index Terms

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Reviews

Reviewer: Richard S. Marcus

The authors report on their development and evaluation of software agents that identify structural elements in documents from which useful information can be data mined and retrieved. The primary example is an algorithm for recognizing tables. Remarkably good results are reported for the analysis of 711 news items in a Usenet newsgroup on the computer hardware business. Of the 151 tables in the text, the algorithm failed to identify only 4, with a like number of pieces of text wrongly identified as tables—that is, it had recall and precision figures of about 97 percent. These results were achieved by selecting optimizing values for three parameters relating to horizontal and vertical spacing and variations from nominal values for these parameters in tabular formats. While some “sensitivity” analysis with respect to these parameters is discussed, it is not clear what kind of average performance can be expected if the parameters are held fixed over a variety of text samples. The authors discuss a variety of issues, such as how to apply the table identification algorithm to actual information access tasks; the appropriate level of specificity and context for development of the algorithms and how to customize the software for more particular cases; and how to integrate these structural techniques into more standard word-based techniques. The authors are to be congratulated for developing table identification algorithms that appear to be impressively effective and for raising issues related to the structural analysis of text. That there is much work to do before such issues are resolved is attested to by the fact that the following (additional) questions remain unanswered: After automatic identification of tables, how well could the software identify the categories of data in the columns and rows__?__ How much do the structural algorithms improve performance in standard tasks such as document retrieval__?__ How much effort should be given to this kind of development of tools for structural analysis of messy, heterogeneous text versus the encouragement of standardization of text representation, for example by encouraging the use of tags for HTML and other standard schemes for identifying structures such as tables and their columns and rows, measurement units, and such<__?__Pub Caret>__?__

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Published In

ACM Transactions on Information Systems Volume 15, Issue 1

Jan. 1997

101 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/239041

Editor:
Robert B. Allen
Bellcore, Morristown, NJ

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 January 1997

Published in TOIS Volume 15, Issue 1

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Tsai WChen ABaldoni R(2008)Service oriented architecture for financial customer relationship managementProceedings of the second international conference on Distributed event-based systems10.1145/1385989.1386027(301-304)Online publication date: 1-Jul-2008
https://dl.acm.org/doi/10.1145/1385989.1386027
Embley DHurst MLopresti DNagy G(2006)Table-processing paradigms: a research surveyInternational Journal of Document Analysis and Recognition (IJDAR)10.1007/s10032-006-0017-x8:2-3(66-86)Online publication date: 9-May-2006
https://doi.org/10.1007/s10032-006-0017-x
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Cited By

Index Terms

Recommendations

Agent-based approach for information gathering on highly distributed and heterogeneous environment

Intelligent Adaptive Information Agents

Agent-based infrastructure and an application to internet information gathering

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